Tobramycin case study – Part 1: Introduction

This case study presents the modeling of the tobramycin pharmacokinetics, and the determination of a priori dosing regimens in patients with various degrees of renal function impairment. It takes advantage of the integrated use of Datxplore for data visualization, Mlxplore for model exploration, Monolix for parameter estimation and Simulx for simulations and best dosing regimen determination.

The case study is presented in 5 sequential parts, that we recommend to read in order:

Part 1: Introduction

Introduction

Tobramycin is an antimicrobial agent of the aminoglycosides family, which is among others used against severe gram-negative infections. Because tobramycin does not pass the gastro-intestinal tract, it is usually administrated intravenously as intermittent bolus doses or short infusions.

Tobramycin is a drug with a narrow therapeutic index. For efficacy, a sufficiently high serum concentration must be achieved. On the other hand, an excess exposure over a long time period bears the risk of nephrotoxicity and ototoxicity.

By developing a population pharmacokinetic model for tobramycin, and relating the pharmacokinetic parameters to easily accessible covariates such as creatinine clearance (representative of the kidney filtration rate) and body weight, the inter-individual variability can be better understood. It is then possible to use this information to a priori determine the best dosing regimen for an effective and safe concentration, using the patient covariate values. This constitutes an example of personalized medicine.

In addition, a rapid assay is available to measure serum tobramycin concentrations. Hence, by monitoring the drug concentration at a few time points after the first dose, the individual PK parameters can be estimated and used to adapt the subsequent doses. The optimal times for the drug monitoring can also be assessed, as an example of optimal design.

The data set presented in this case study has been originally published in:

Workflow

We will first explore the data set with Datxplore to better grasp its properties. We will then go through the model building process. For this Monolix will be used, to implement the model in the Mlxtranlanguage, estimate the parameters, and to assess the model using the built-in diagnostic plots. As the model becomes more complex, the advantage of the visual exploration of the model parameters influence using Mlxplore will also be shown. Once a satisfactory model is obtained, simulations of new dosing regimens for specific patients or patient populations will be done using Simulx.